Rubin-LSST

Abstract:

In recent years, the black hole candidate X-ray binary system H1743-322 has undergone outbursts and it has been observed with X-ray and radio telescopes. We present 1.3 GHz MeerKAT radio data from the ThunderKAT Large Survey Project on radio transients for the 2018 outburst of H1743-322. We obtain seven detections from a weekly monitoring programme and use publicly available Swift X-ray Telescope and MAXI data to investigate the radio/X-ray correlation of H1743-322 for this outburst. We compare the 2018 outburst with those reported in the literature for this system and find that the X-ray outburst reported is similar to previously reported 'hard-only' outbursts. As in previous outbursts, H1743-322 follows the 'radio-quiet' correlation in the radio/X-ray plane for black hole X-ray binaries, and the radio spectral index throughout the outburst is consistent with the 'radio-quiet' population.

Abstract:

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label. Our posteriors are well-calibrated (e.g. for predicting bars, we achieve coverage errors of 11.8 per cent within a vote fraction deviation of 0.2) and hence are reliable for practical use. Further, using our posteriors, we apply the active learning strategy BALD to request volunteer responses for the subset of galaxies which, if labelled, would be most informative for training our network. We show that training our Bayesian CNNs using active learning requires up to 35–60 per cent fewer labelled galaxies, depending on the morphological feature being classified. By combining human and machine intelligence, Galaxy zoo will be able to classify surveys of any conceivable scale on a time-scale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution.

Abstract:

MAXI J1535-571 is a Galactic black hole candidate X-ray binary that was discovered going into outburst in 2017 September. In this paper, we present comprehensive radio monitoring of this system using the Australia Telescope Compact Array, as well as the MeerKAT radio observatory, showing the evolution of the radio jet during its outburst. Our radio observations show the early rise and subsequent quenching of the compact jet as the outburst brightened and then evolved toward the soft state. We constrain the compact jet quenching factor to be more than 3.5 orders of magnitude. We also detected and tracked (for 303 days) a discrete, relativistically moving jet knot that was launched from the system. From the motion of the apparently superluminal knot, we constrain the jet inclination (at the time of ejection) and speed to ≤45° and ≥0.69 c, respectively. Extrapolating its motion back in time, our results suggest that the jet knot was ejected close in time to the transition from the hard intermediate state to soft intermediate state. The launching event also occurred contemporaneously with a short increase in X-ray count rate, a rapid drop in the strength of the X-ray variability, and a change in the type-C quasi-periodic oscillation (QPO) frequency that occurs >2.5 days before the first appearance of a possible type-B QPO.